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Vehicles equipped with one or several functions of Advanced Driver Assistant System (ADAS) and autonomous driving (AD) technology are more mature and prevalent nowadays. Vehicles being smarter and driving being easier is an unstoppable trend. In the near future, intelligent vehicles will be mass produced and running on the road. However, before the mass-production of intelligent vehicles, a lot of experimental tests and validations need to be carried out to insure the safety and reliability of ADAS and AD technology. Although the road test of real vehicles is the most reliable and accurate test method, it cannot meet the need of rapid development of technology research due to high time and financial cost. Therefore, a high-efficient design and evaluation methodology for ADAS and AD development and test is a must. In this paper, a virtual co-simulation platform based on MATLAB/Simulink, OpenModelica and Unity 3D game engine (MOMU) is proposed.

Recently, electro-hydraulic brake systems (EHB) has been developed to take place of the vacuum booster, having the advantage of faster pressure build-up and continuous pressure regulation. In contrast to the vacuum booster, the pressure estimation for EHB is worth to be studied due to its abundant resource (i.e. electric motor) and cost-effective benefit. This work improves an interconnected pressure estimation algorithm (IPEA) based on actuator characteristics by introducing the vehicle dynamics and validates it via vehicle tests. Considering the previous IPEA as the prior pressure estimation, the wheel speed feedback is used for modification via a proportional-integral (PI) observer. Superior to the IPEA based on actuator characteristics, the proposed PEA improves the accuracy by more than 20% under the mismatch of pressure-position relation.

In the autonomous driving perception task, the accuracy of target detection is an essential evaluation, especially for small targets. In this work, we propose a multi-sensor fusion neural network that combines radar and image data to improve the confidence level of the camera when detecting targets and the accuracy of the prediction box regression. The fusion network is based on the basic structure of single-shot multi-box detection (SSD). Inspired by the attention mechanism in image processing, our work incorporates the a priori knowledge of radar detection in the convolutional block attention module (CBAM), which forms a new attention mechanism module called radar convolutional block attention module (RCBAM). We add the RCBAM into the SSD target detection network to build a deep neural network fusing millimeter-wave radar and camera.

Water content estimation is a key problem for studying the PEM fuel cell. When several hundred fuel cells are connected in serial and their active surface area is enlarged for sufficient power, the difference between cells becomes significant with respect to voltage and water content. The voltage of each cell is measurable by the cell voltage monitor (CVM) while it is difficult to estimate water content of the individual. Resistance of the polymer electrolyte membrane is monotonically related to its water content, so that the new online high frequency resistance (HFR) measurement technique is investigated to identify the uniformity of water content between cells and analyze its sensitivity to operating conditions in this paper. Firstly, the accuracy of the proposed technique is experimentally validated to be comparable to that of a commercialized electrochemical impedance spectroscopy (EIS) measurement equipment.

Crashworthiness is one of the most important performances of vehicles, and the front rails are the main crash energy absorption parts during the frontal crashing process. In this paper, the front rail was simplified to a thin-walled beam with a cross section of single-hat which was made of steel and aluminum. And the two boards of it were connected by riveting without rivets. In order to optimize its crashworthiness, the thickness (t), radius (R) and the rivet spacing (d) were selected as three design variables, and its specific energy absorption was the objective while the average impact force was the constraint. Considering the error of manufacturing and measurements, the parameters σs and Et of the steel were selected as the uncertainty variables to improve the design reliability. The algorithm IP-GA and the approximate model-RBF (Radial Basis Function) were applied in this nonlinear uncertainty optimization.

The transient characteristics of combustion and emissions during the engine start up at different higher cranking speeds for hybrid electric vehicle (HEV) applications were presented in this paper. Cycle-by-cycle analysis was done for each start up case. Intake air mass during the first several cycles decrease as the engine was cranked at higher speed. Ignition timing is delayed with higher cranking speed, which leads to an increase of exhaust temperature. For various start up cases, similar quantity of fuel is injected at the first cycle, but the ignition timing is significantly delayed to meet the acceleration requirement when cranking speed enhanced. Because of the deterioration of intake charge, the air-fuel mixture is over-enriched in the first several cycles for the cases at higher cranking speed. With cranking speed is increased, the in-cylinder residual gas fraction rises, which leads to poor combustion and decrease of mass fraction of burned fuel.

The motor has vibration characteristics of order and multi-band in the frequency domain, which is different from the internal combustion engine when it is used as the vehicle’s drive. These characteristics cannot be briefly attenuated by general methods, but make the phononic crystal (PC) an ideal solution to eliminate the vibration transmission of the motor, because the concentrated vibration peak can easily be blocked by the bandgap. In this paper, one dimensional locally resonant phononic crystal (LRPC) which has low-frequency bandgaps are arranged on the automotive subframe to absorbing vibration. The partial coherence analysis is used to analyze the transfer characteristic of vibration on the subframe. Then, 6 main paths are selected from the 18 vibration transmission paths, based on its high ratio of partial coherence coefficient in a certain frequency, and the arranged position, the spring stiffness and the resonator’s mass of the LRPCs are chosen based on this result.

Carbon fiber reinforced plastic (CFRP) composite materials have gained particular interests due to their high specific modulus, high strength, lightweight and perfect corrosion resistance. However, in reality, CFRP composite materials cannot be used alone in some critical places such as positions of joints with hinges, locks. Therefore, metal reinforcements are usually necessary in local positions to prevent structure damage. Besides, if uncertainties present, obtained optimal structures may experience in failures as the optimization usually pushes solutions to the boundaries of constraints and has no room for tolerance and uncertainties, so robust optimization should be considered to accommodate the uncertainties in practice. This paper proposes a mixed topology method to optimize metal and carbon fiber reinforced plastic composite materials simultaneously under nondeterministic load with random magnitude and direction.

The Argon Power Cycle engine is a novel concept for high efficiency and zero emission through the replacement of N2 by Ar. However, the higher in-cylinder temperature and pressure as by-products cause heavier knock. The anti-knock strategies, such as reducing compression ratio and retarding ignition time, offset the efficiency increased by the Argon Power Cycle. Therefore, knock control becomes the most urgent task for the Argon Power Cycle engine development. The anti-knock methods, including fuel replacement, ultra-lean combustion, high dilution combustion, and water injection, were considered. The simulated ignition delay times were used to evaluate the probability of knock. The Otto cycle, combined with chemical equilibrium, was utilized to confirm the effect on the thermal conversion efficiency and each in-cylinder thermodynamic state parameter. The results show that the ignition delay times increase by a factor of two when the Ar dilution ratio increases from 79% to 95%.

As one of the most crucial components in electric vehicles, power batteries generate abundant heat during charging and discharging processes. Thermal management system (TMS), which is designed to keep the battery cells within an optimum temperature range and to maintain an even temperature distribution from cell to cell, is vital for the high efficiency, long calendar life and reliable safety of these power batteries. With the desirable features of low system complexity, light weight, high energy efficiency and good battery thermal uniformity, thermal management using composite phase change materials (PCMs) has drawn great attention in the past fifteen years. In the hope of supplying helpful guidelines for the design of the PCM-based TMSs, this work begins with the summarization of the most commonly applied heat transfer enhancement methods (i.e., the use of thermally conductive particles, metal fin, expanded graphite matrix and metal foam) for PCMs by different researchers.

The multi-body dynamics simulation and physical iteration were carried out based on the 4-channel road simulation bench, the solution of fatigue test bench which was suitable for cab with frame and suspension was designed. Large load and displacement above the suspension can be loaded on the test bench, and the same weak position of cab exposed on the road test can be assessed well on the fatigue test bench. The effectiveness of the bench test solution was verified though comparative study. And it has important reference for the same type of cab assembly with suspension in the fatigue bench test. According to the durability specifications of cab assembly, a multi-body dynamics model with a satisfactory accuracy was built. And the fixture check and virtual iteration analysis were used to verify the effectiveness of the solution. According to the road load signal analysis and multi-body dynamics analysis results, the test bench with linear guide and spherical joint was built.

The cab is an essential part of a light truck, and its fatigue durability performance plays an important role in the design and development stage. Accelerated fatigue bench test has been widely applied to product development of carmakers for its low cost and short development cycle. However, in reality, interference exists generally in torsional conditions for the light truck cab when tested on the 4-post vehicle road simulation system. To solve this problem and minimize the lateral force applied on the hydraulic cylinders, the direction and size combinations of displacement release about front and rear suspensions were discussed based on multi-body dynamics simulation and fixture design theory in this paper. Through comparative study, the optimum design and layout scheme of fixtures was determined to conduct the next test procedure. The weak positions of the light truck cab were firstly predicted by utilizing finite element method (FEM) and fatigue analysis theory.

With the rapid development of computing technology, high-speed photography system and image processing recently, in order to meet growing dynamic mechanical engineering problems demand, a brief description of advances in recent research which solved some key problems of dynamic photo-elastic method will be given, including:(1) New digital dynamic photo-elastic instrument was developed. Multi-spark discharge light source was replaced by laser light source which was a high intensity light source continuous and real-time. Multiple cameras shooting system was replaced by high-speed photography system. The whole system device was controlled by software. The image optimization collection was realized and a strong guarantee was provided for digital image processing. (2)The static and dynamic photo-elastic materials were explored. The new formula and process of the dynamic photo-elastic model materials will be introduced. The silicon rubber mold was used without the release agent.

Centrifugal Pendulum Vibration Absorber (CPVA for short) is used to absorb torsional vibrations caused by the shifting motion of the engine. It is increasingly used in modern powertrains. In the research of the dynamic characteristics of the CPVA, it is necessary to obtain the real motion of the pendulum to compensate the fitting performance of mathematical model. The usual method is to install an angle sensor to measure the movement of the pendulum. On the one hand, the installation of the sensor will affect its movement to a certain extent, so that the measurement results do not match the actual motion. On the other hand, the motion of the pendulum is not only the rotational motion around the rotational axis of the CPVA rotor, but also has translation relative to it. As a result, it is difficult to obtain accurate motion only by the angle sensor. We proposed a non-contact centrifugal pendulum motion measurement method.

The nonlinear characteristics impact of multi-staged stiffness clutch damper on the vehicle creeping is investigated by using the lumped-parameter modeling method as a certain mass-production passenger sedan is taken as the research subject. Firstly, a quasi-transient engine model of an inline four-cylinder and four-stroke engine, based on measured data of cylinder gas pressure versus crankshaft angle, is derived. Effective output torque is acquired and as the input excitation to the driveline system. Secondly, a 12-DOF (Degree of Freedom) nonlinear and branched powertrain system and vehicle longitudinal dynamics model is established. The differential mechanism characteristics and dynamic tire property based on the LuGre tire model are considered. Then, for a traditional two-staged stiffness clutch damper in consideration of hysteresis characteristics, vehicle powertrain system responses in both the time and frequency domain are obtained.

In order to explore approaches for improving object detection accuracy in intelligent vehicle system, we exploit super-resolution techniques. A novel method is proposed to confirm the conjecture whether some popular super-resolution networks used for environmental perception of intelligent vehicles and robots can indeed improve the detection accuracy. COCO dataset which contains images from complex ordinary environment is utilized for the verification experiment, due to it can adequately verify the generalization of each algorithm and the consistency of experimental results. Using two representative object detection networks to produce the detection results, namely Faster R-CNN and YOLOv3, we devise to reduce the impact of resizing operation. The two networks allow us to compare the performance of object detection between using original and super-resolved images. We quantify the effect of each super-resolution techniques as well.

Smart electric vehicles need more accurate and more timely information as well as control than traditional vehicles, which depends on great environmental sensors such as millimeter-wave radar. In this way, the electromagnetic compatibility of whole vehicle would confront more serious challenges because of its high frequency range. Thus, this paper studies the electromagnetic distribution and electromagnetic interference suppression of smart electric vehicles with the followings. Firstly, the millimeter wave radar is modeled and optimized. Micro strip patch antenna, with small size, light mass and low cost, is used as array element of antenna. Millimeter wave radar is modeled and simulated step by step from array element to line array to planar matrix. Then the Cross Shape - Uniplanar Compact - Electromagnetic Band Gap (CS-UC-EBG) structure is deployed to optimize its electromagnetic characteristics, based on finite time domain difference model theory.

Electromechanical Braking System (EMB) stops the wheel by motor and related enforce mechanism to drive braking pads to clamp the friction plate. It is compact in sized as well as faster in response, which solves the issue of potential leakage and slows response of traditional hydraulic brake system. The institutions at home and abroad have put forward all kinds of new structural schemes of EMB. At present, there are various EMB structural schemes, but the analysis and evaluation of these schemes are relatively few. In this paper, on the basis of a large number of research, the EMB actuator is modular decomposed according to function ,then the parametric 3D model library of each function module is established. According to brake requirements of the target vehicle, a development platform is set up to match EMB actuator structure scheme quickly.

Compared with the low-frequency ignition order of mechanical and combustion noise of an internal combustion engine, the noise of electric drive assembly of electric vehicles is mainly the high-frequency whining noise generated by electromagnetic forces of motors and gear meshing of reducers, as well as the high-frequency umbrella-shape noise generated by DC/AC pulse width modulation. Although the radiated sound power of these high frequency noise is far less than that of an internal combustion engine, the high frequency noise of the motor and the reducer is subjectively quite annoying. This paper studies the characteristics of electromagnetic noise of a permanent magnet synchronous motor in an electric car. By testing and analyzing of noise sources of an electric motor in the car and on a test rig, the spatial order characteristics and amplitude-frequency characteristics of the electromagnetic forces are revealed. The noise orders are multiples of the number of motor poles.

Accurate prediction temperature variation of electric drive transmission (EDT) can effectively monitor its abnormal temperature rise that may occur under high speed and heavy load working conditions, so as to ensure the vehicles’ safe operation. In this paper, combined with real temperature and input/output characteristic data collected from EDT test platform under different working conditions, a spatio-temporal relationship dynamic graph convolution neural network based on least square method (OLS-DRGCN) for temperature prediction is proposed. Firstly, OLS is used to estimate the EDT’s internal temperature based on partial sensor information as the input of OLS-DRGCN. Secondly, the spatial dependence relationship of each temperature node is dynamically learned through node embedding and the dynamic thermal network topology of EDT is constructed. Meanwhile, the timing rule of each temperature node is obtained through the gated recurrent unit.